Review of Quantitative Finance and Accounting

, Volume 13, Issue 4, pp 323–345

Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

  • Emel Kahya
  • Panayiotis Theodossiou


The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit.

financial distress models linear discriminant analysis logit model non-stationary financial ratios time-series CUSUM vector autoregressive process 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Altman, E.I., “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance 23, 589-609, (1968).Google Scholar
  2. Altman, E.I., R.G. Haldeman, and P. Narayanan, “Zeta Analysis, a New Model for Identifying Bankruptcy Risk of Corporation.” Journal of Banking and Finance 1, 29-54, (1977).Google Scholar
  3. Amemiya, T., “Qualitative Response Models: A Survey.” Journal of Economic Literature 19, 1483-1536, (1981).Google Scholar
  4. Beaver, W.H., “Financial Ratios as Predictors of Failure.” Journal of Accounting Research Supplement, 71-111, (1966).Google Scholar
  5. Chu, C.J. and H. White, “A Direct Test for Changing Trend.” Journal of Business and Economics Statistics 10, 289-299, (1992).Google Scholar
  6. Dickey, D.A. and W.A. Fuller, “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74, 427-431, (1979).Google Scholar
  7. Edmister, R.O., “An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction.” Journal of Financial and Quantitative Analysis 7, 1477-1493, (1972).Google Scholar
  8. Efron, B., The Jackknife, the Bootstrap and Other Resampling Plans, SIAM, Philadelphia, Pennsylvania, 1982.Google Scholar
  9. Gombola, M.J., M.E. Haskins, J.E. Ketz, and D. Williams, “Cash Flow in Bankruptcy Prediction.” Financial Management 16, 55-65, (1987).Google Scholar
  10. Johansen, S., Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models, Advanced Texts in Econometrics, Oxford University Press:, New York, 1995.Google Scholar
  11. Judge, G.G., W.E. Griffiths, R.C. Hill, Helmut Lutkepohl, and Tsoung-Chao Lee, The Theory and Practice of Econometrics, John Wiley: New York, 1985.Google Scholar
  12. Lee, C.F. and C. Wu, “Expectation Formation and Financial Ratio Adjustment Processes.” The Accounting Review 63, 292-306, (1988).Google Scholar
  13. Lo, A.W., “Logit Versus Discriminant Analysis: A Specification Test and Application to Corporate Bankruptcies.” Journal of Econometrics 31, 151-178, (1986).Google Scholar
  14. Lorden, G., “Procedures for Reacting to a Change in Distribution.” Annals of Mathematical Statistics 42, 1897-1908, (1971).Google Scholar
  15. Marks, S. and O.J. Dunn, “Discriminant Functions when Covariance Matrices are Unequal.” Journal of the American Statistical Association 69, 555-559, (1974).Google Scholar
  16. McLachlan, G.J., “Estimation of Error Rates.” Discriminant Analysis and Statistical Pattern Recognition Wiley, New York, Ch. 10, 337-377, (1992).Google Scholar
  17. Neftci, S.N., “Optimal Prediction of Cyclical Downturns.” Journal of Economic Dynamics and Control 6, 225-241, (1982).Google Scholar
  18. Neftci, S.N., “A Note on the Use of Local Maxima to Predict Turning Points in Related. Series.” Journal of the American Statistical Association 80, 553-557, (1985).Google Scholar
  19. Ohlson, J.A., “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research 18, 109-131, (1980).Google Scholar
  20. Siegmund, D., Sequential Analysis: Tests and Confidence Intervals, Springer Series in Statistics, Springer-Verlag, New York, 1985.Google Scholar
  21. Pastena, V. and W. Ruland, “The Merger/Bankruptcy Alternative.” The Accounting Review 61, 288-301, (1986).Google Scholar
  22. Pollak, M. and D. Siegmund, “Approximations to the Sample Size of Certain Sequential Tests.” The Annals of Statistics 3, 1267-1282, (1975).Google Scholar
  23. Theodossiou, P., “Predicting Shifts in the Mean of a Multivariate Time Series Process: An Application in Predicting Business Failures.” Journal of the American Statistical Association 88, 441-449, (1993).Google Scholar
  24. Theodossiou, P., and E. Kahya, “Non-Stationarities in Financial Variables and the Prediction of Business Failures.” 1996 Proceedings of the Business and Economic Statistics Section. American Statistical Association 130-133, (1996).Google Scholar
  25. Theodossiou, P., E. Kahya, G.C. Philippatos, and R. Saidi, “Financial Distress Corporate Acquisitions: Further Empirical Evidence.” Journal of Business Finance and Accounting 23, (1996).Google Scholar
  26. Wecker, W.E., “Prediction of Turning Points.” Journal of Business 52, 35-50, (1979).Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Emel Kahya
    • 1
  • Panayiotis Theodossiou
    • 2
  1. 1.School of BusinessRutgers UniversityCamden
  2. 2.School of BusinessRutgers UniversityCamden

Personalised recommendations